{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113265a90>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 2, 3]\n",
    "b = [4, 5, 6]\n",
    "plt.plot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1133bb8d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V1P5DBTz97nL+9Pla2jepx//efAbn92gddFkiUg0o+CuhT1ZuY8KMdDbuOsCN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\nc6XycAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fcef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113545e48>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V1P5DBTz97nL+9Pla2jepx//efAbn92gddFkiUg0o+CuhT1ZuY8KMdDbuOsCN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\nc6XycAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133f7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 22.76 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 616 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113dda5c0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zW+rub9fd192nElkioqioKPGuAN8Iu/dW8/t/fMx9b6+gXU4WvzxrIDlZOoFK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\nSIpR4hcRSTFK/CIiKeb/AzcbdUa88QP5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e26160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a, b, '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11412c780>,\n",
       " <matplotlib.lines.Line2D at 0x11412c978>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1BdjZdIgO4bHYl8djXx6PF7yaY/GGzGzr7sVainssiIj+zOxrOkcn8Fjsy+OxL4/HC0br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\n9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11409e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = [10,8,6]\n",
    "d = [1,8,3]\n",
    "plt.plot(a,b, 'r--', c,d, 'b*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.arange(0.0, 2.0, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = np.sin(t*np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11526ec18>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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www8AdOnShcqVK7Pnnnvy97//fcdxYsW/r4X2yy8wcaJ92sX46/P27XbBM3FiAasgrl9v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\nMfH/kRYW2OrFmo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153012e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,s,'r--',label='aaaa')\n",
    "plt.plot(t*2, s, 'b--', label='bbbb')\n",
    "plt.xlabel('this is x')\n",
    "plt.ylabel('this is y')\n",
    "plt.title('this is a demo')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
